Related papers: Universal Platform for Scalable Semiconductor-Supe…
Modular architectures are a promising approach to scale quantum devices to the point of fault tolerance and utility. Modularity is particularly appealing for superconducting qubits, as monolithically manufactured devices are limited in both…
Spiking Neural Networks (SNNs) have gained significant traction in both computational neuroscience and artificial intelligence for their potential in energy-efficient computing. In contrast, artificial neural networks (ANNs) excel at…
Hybrid semiconductor-superconductor InAs-Al nanowires with uniform and defect-free crystal interfaces are one of the most promising candidates used in the quest for Majorana zero modes (MZMs). However, InAs nanowires often exhibit a high…
Solid-state quantum emitters coupled to integrated photonic nanostructures are quintessential for exploring fundamental phenomena in cavity quantum electrodynamics and widely employed in photonic quantum technologies such as non-classical…
Non-Abelian anyons such as Majorana zero modes (MZMs) have the potential to enable fault-tolerant quantum computing through topological protection. Experimentally reported InSb topological superconductor nanowires (TSNW) are investigated…
Topological superconductivity can be engineered in semiconductors with strong spin-orbit interaction coupled to a superconductor. Experimental advances in this field have often been triggered by the development of new hybrid material…
Dense photonic integration promises to revolutionize optical computing and communications. However, efforts towards this goal face unacceptable attenuation of light caused by surface roughness in microscopic devices. Here we address this…
The rapid scaling of deep neural networks comes at the cost of unsustainable power consumption. While optical neural networks offer an alternative, their capabilities remain constrained by the lack of efficient optical nonlinearities. To…
Despite tremendous progress in the research on self-assembled nanotechnological building blocks such as macromolecules, nanowires, and two-dimensional materials, synthetic self-assembly methods bridging nanoscopic to macroscopic dimensions…
Spiking Neural Networks (SNNs) operate with asynchronous discrete events (or spikes) which can potentially lead to higher energy-efficiency in neuromorphic hardware implementations. Many works have shown that an SNN for inference can be…
We develop a unified numerical approach for modeling semiconductor-superconductor heterostructures. Our approach takes into account on equal footing important key ingredients: proximity-induced superconductivity, orbital and Zeeman effect…
Spiking Neural Networks (SNNs) may offer an energy-efficient alternative for implementing deep learning applications. In recent years, there have been several proposals focused on supervised (conversion, spike-based gradient descent) and…
The lack of energy dissipation and abrupt electrical phase transition of superconductors favorite them for nanoscale technologies, including radiation detectors, and quantum technologies. Moreover, understanding the nanoscale behavior of…
Tunneling spectroscopy is widely used to examine the subgap spectra in semiconductor-superconductor nanostructures when searching for Majorana zero modes (MZMs). Typically, semiconductor sections controlled by local gates at the ends of…
A novel high-fan-in differential superconductor neuron structure designed for ultra-high-performance Spiking Neural Network (SNN) accelerators is presented. Utilizing a high-fan-in neuron structure allows us to design SNN accelerators with…
We study layered systems and heterostructures of s-wave superconductors by means of a suitable generalization of Dynamical Mean-Field Theory. In order to reduce the computational effort, we consider an embedding scheme in which a relatively…
Subwavelength semiconductor nanowires have recently attracted interest for photonic applications because they possess various unique optical properties and offer great potential for miniaturizing devices. However, realizing tight light…
Semiconductor nanowire (NW) lasers are a promising technology for the realisation of coherent optical sources with extremely small footprint. To fully realize their potential as building blocks in on-chip photonic systems, scalable methods…
Hybrid superconductor/semiconductor devices constitute a powerful platform to investigate the emergence of new topological state of matter. Among all possible semiconductor materials, InAs represents a promising choice, owing to its high…
Vapor-liquid-solid (VLS) route and its variants are routinely used for scalable synthesis of semiconducting nanowires yet the fundamental growth processes remain unknown. Here, we employ atomic-scale computations based on model potentials…